DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the application filed on 10--/21/2025 for application 17/951,001. Claim 1 – 14 and 16 – 20 are pending and have been examined.
Claim 1 – 3, 13, 17 – 20 are amended.
Claim 15 is canceled.
Response to Amendment
Applicant’s amendment filed on 10/21/2025 has been entered.
Response to Argument
Applicant's remark filed on 10/21/2025 has been fully considered but they are not persuasive.
Regarding claim rejection under 101 section, applicant stated that the amended limitation of “controlling a vehicle based on the respective predicted multi-state classification generated for each of the one or more target agents” recite patent-eligible subject matter. Examiner notes that during the short review in the interview dated 10/10/2025, the recited limitation seems overcome 101 rejection. However, after further consideration with the amended Claim 13: “controlling the vehicle … comprises providing data specifying the respective predicted multi-category classification … to a planning system of the vehicle to generate planning decision”, the controlling of the vehicle is now interpreted as non-patent eligible limitations, especially, “providing data” recites WURC recognized in MPEP 2106.05(d)(i) of “receiving or transmitting data over a network” and “generate decision” recites a mental process of observation, evaluation and judgement. For further detail, refer to the claim rejection under 101 section. In addition, the specification 0013 of the instant application describe the controlling of the vehicle in a similar manner: “to control the autonomous vehicle, i.e., to plan the future motion of the vehicle”. Thus, “controlling a vehicle”, within BRI, is interpreted as an additional step of the computing system of the vehicle that performs the planning/decision making step of the future motion of the vehicle based on the generated classifications. Thus, examiner maintains the corresponding rejection.
Applicant’s arguments with respect to claim rejection under prior art section are persuasive. Therefor the rejection has been withdrawn. However upon further consideration, a new ground(s) of rejection is made with the same reference.
Applicant stated that “the cited portion of Weinstein-Raun does not disclose or suggest generating a ‘predicted multi-category classification’ that includes ‘a respective score for each of a plurality of categories including an active participant category that indicates that the target agent is an active participant of the traffic in the environment and two or more stationary non-participant categories.’ In particular, the cited portion of Weinstein-Raun does not disclose or suggest that the ‘two or more stationary non-participant categories [] represent different categories of stationary non-participants of traffic in the environment.’” Examiner notes that the amended claims describes a prediction/calculation method such that, for each target agent, predict/calculate a score on each of the multiple categories/states (Claim 1), and the respective score represents the likelihood that the target agent is in the category (Claim 3). Weinstein-Raun also describe such prediction/calculation method in Fig. 7 & 0089 – 0100, “dynamic Bayesian network 700 includes various nodes 702-736”, “the nodes 702-736 are updated using simple rules of probability, in particular the law of total probability, namely: p(A)=p(AIB)p(B)+p(AI-B) p(-B)”, “the target vehicle is parked by the side of the road or in a parking lot, waiting at a bus stop, stopped to make a delivery, double parked, and so on”, “in various embodiments, the intermediate nodes 726-734 include: (i) a blocked state 726 representing whether the target vehicle is blocked from movement; (ii) a pulled over state 728 representing whether the target vehicle is pulled over ( e.g., in certain embodiments, along the roadway); (iii) a motion state 730 pertaining to motion of the target vehicle ( e.g., as to a magnitude and direction of movement of the target vehicle, in certain embodiments); (iv) a passable state 732 (e.g., as to whether the vehicle 10 is able to successfully maneuver around the target vehicle, in certain embodiments); and (vi) an apparent activity state 734 (e.g., as to an indication of whether the target vehicle is active or inactive)”; i.e., the system evaluates each of the target agent on multiple states/categories using probability / likelihood that the targe agent is likely in each of the multiple states/categories. The stationary non-participant state/categories includes at least blocked for movement, pulled over, passable state. For the pulled over state, the system may determine to maintain its current path (0086) and for passable vehicle, the system may determine to drive around the target vehicle (0073). Thus, Weinstein-Raun fulfills the claimed limitations.
For further details refer to the claim rejection under 35 U.S.C. 102 & 103 section.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 – 14 and 16 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Step 1 Analysis
Claim 1 is directed to a method, which is one of the statutory categories.
Step 2A Prong One Analysis:
Claim 1 recites the abstract ideas in the following limitations:
generating, based on processing the context data … a respective predicted multi-category classification for each of one or more target agents of the plurality of agents in the environment, the respective predicted multi-category classification comprising a respective score for each of a plurality of categories including an active participant category that indicates that the target agent is an active participant of the traffic in the environment and two or more stationary non-participant categories that represent different categories of stationary non-participant of traffic in the environment
controlling a vehicle based on the respective predicted multi-state classification generated for each of the one or more target agents
The steps of generating recite observation, evaluation and judgement mental processes and can practically be performed in human mind with or without physical aid and thus falls under the mental processes group of abstract idea.
The controlling of the vehicle, as claimed in Claim 3 and described in the specification 0013, refer to the generating of decisions of future trajectory. Thus, the controlling of the vehicle recites observation, evaluation and judgement mental processes and can practically be performed in human mind with or without physical aid and thus falls under the mental processes group of abstract idea.
And thus, the claim falls within judicial exception of abstract idea and requires further analysis under Step 2A Prong Two.
Step 2A Prong Two Analysis:
Claim 1 recites the following additional elements along with the abstract ideas:
obtaining context data characterizing an environment, the context data comprising data characterizing a plurality of agents in the environment;
using a neural network having a plurality of network parameters
The step of obtaining is recited at high level generality which add insignificant extra solution activity to the judicial exception.
The additional element of using a neural network is recited in high generality and amounts to no more than a recitation of the words "apply it" (or an equivalent), or no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Claim 1 does not integrate the abstract idea into a practical application. Claim 1 directs to abstract idea.
Step 2B Analysis:
The steps of obtaining is well-understood, routine, conventional activity recognized in MPEP 2106.05(d)i - receiving or transmitting data over a network.
The additional element of using a neural network is recited in high generality and amounts to no more than a recitation of the words "apply it" (or an equivalent), or no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Claim 1 does not contribute inventive concept. Claim 1 is not eligible.
Regarding Claim 2 – 14, 16 – 20,
Claim 2 – 14, 16 – 20 fails to remedy these deficiencies and thus rejected with the same reason.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 – 5, 13 – 14, 17 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Weinstein-Raun, US20180079423.
Regarding Claim 1, Weinstein-Raun discloses: A method performed by one or more computers (0033, “any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), a field programmable gate-array (FPGA), an electronic circuit, a processor ( shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.”), the method comprising: obtaining context data characterizing an environment, the context data comprising data characterizing a plurality of agents in the environment (0042, “the intent of the target vehicle (agents) (and/or its operator) may be determined based on a number of observed characteristics pertaining to the target vehicle … observed characteristics (e.g., movement or non-movement) of a lead vehicle in front of the target vehicle; operation of the blinkers and/or other turn signal indicators for the target vehicle; a type of vehicle represented by the target vehicle (e.g., whether the target vehicle is a bus, delivery vehicle, or the like), whether the target vehicle is currently moving; information as to an intersection or roadway in which the target vehicle is travelling (e.g., whether a stop sign or traffic light is present, whether an emergency vehicle is travelling nearby, or whether there is an observed blockage in traffic, and if so whether the blockage in traffic is passable, and so on”);
generating, based on processing the context data using a neural network having a plurality of network parameters (0109, “the various modules and systems described above may be implemented as one or more machine learning models … trained to perform … multiclass classification … artificial neural network ANN”; i.e., by tuning parameters, machine learning model is trained to perform specific task), a respective predicted multi-category classification for each of one or more target agents of the plurality of agents in the environment, the respective predicted multi-category classification comprising a respective score for each of a plurality of categories including an active participant category that indicates that the target agent is an active participant of the traffic in the environment and two or more stationary non-participant categories that represent different categories of stationary non-participant of traffic in the environment (Fig. 7 & 0089 – 0100, “dynamic Bayesian network 700 includes various nodes 702-736 (states/categories)”, “the nodes 702-736 are updated using simple rules of probability, in particular the law of total probability (score), namely: p(A)=p(AIB)p(B)+p(AI-B) p(-B)”, “the target vehicle is parked by the side of the road or in a parking lot, waiting at a bus stop, stopped to make a delivery, double parked, and so on”, “in various embodiments, the intermediate nodes 726-734 include: (i) a blocked state 726 representing whether the target vehicle is blocked from movement; (ii) a pulled over state 728 representing whether the target vehicle is pulled over ( e.g., in certain embodiments, along the roadway); (iii) a motion state 730 pertaining to motion of the target vehicle ( e.g., as to a magnitude and direction of movement of the target vehicle, in certain embodiments); (iv) a passable state 732 (e.g., as to whether the vehicle 10 is able to successfully maneuver around the target vehicle, in certain embodiments); and (vi) an apparent activity state 734 (e.g., as to an indication of whether the target vehicle is active or inactive)”; i.e., the system evaluates each of the target agent on multiple states/categories using probability / likelihood that the targe agent is likely in each of the multiple states/categories. The stationary non-participant state/categories includes at least blocked for movement, pulled over, passable state); and
controlling a vehicle based on the respective predicted multi-state classification generated for each of the one or more target agents (0086, “the target vehicle comprises a vehicle that is stopped within or proximate the same lane … contact would be likely if the target vehicle were to remain stopped and the vehicle 10 were to maintain its current path”; i.e., based on the determined multiple states/categories, the system control the vehicle with different policy; 0073, “change the current path to drive around the target vehicle if the target vehicle is inactive”).
Regarding Claim 2, Weinstein-Raun teaches all the limitation of Claim 1. Weinstein-Raun further teach: the two or more stationary nonparticipant categories comprise two or more of: a first stationary non-participant category that indicates that the target agent is a double parked vehicle in the environment, a second stationary non-participant category that indicates that the target agent is a parked vehicle in the environment, a third stationary non-participant category that indicates that the target agent is a pulled over vehicle in the environment, or a fourth stationary non-participant category that indicates that the target agent is a stalled vehicle in the environment (refer to the mapping in Claim 1, the system identifies the possibility of at least the following two non-participant states/categories that the target vehicle is pulled over, the target vehicle is blocked (stalled) ).
Regarding Claim 3, Weinstein-Raun teaches all the limitation of Claim 2. Weinstein-Raun further teach: the respective score for each of the plurality of categories represents a likelihood that the target agent is in the category (refer to the mapping in Claim 1 & 0089, “the nodes (states/categories) 702-736 are updated using simple rules of probability (score)”; the probability represents the likelihood that the target vehicle is in the state/category).
Regarding Claim 4, Weinstein-Raun teaches all the limitation of Claim 1. Weinstein-Raun further teach: the context data characterizing the environment comprises data characterizing a plurality of road features in the environment and data characterizing one or more traffic light signals in the environment (refer to the mapping in Claim 1 & 0042, roadway, intersection, traffic sign, traffic lights are all among the environmental data; 0038, “a red light (traffic light signal), a stop sign, and so on”).
Regarding Claim 5, Weinstein-Raun teaches all the limitation of Claim 4. Weinstein-Raun further teach: the data characterizing each of the plurality of road features comprises one or more road feature vectors characterizing the road feature; the data characterizing each of the plurality of agents comprises one or more agent vectors characterizing the agent; and the data characterizing each of the plurality of traffic light signals comprises one or more traffic light vectors characterizing the traffic light signal (0109, “As mentioned briefly, the various modules and systems described above may be implemented as one or more machine learning models”; Weinstein-Raun teaches the use of computerized machine learning model to process the input/sensor/vehicle data for inference. One of skilled in the art would recognize that a collection of values is a vector in machine learning field. Such understanding can be easily found online for example “vector is a general term with many uses. In this case, think of it as a list of values or a row in a table.”, StackOverflow, “What is vector in terms of machine learning”).
Regarding Claim 13, Weinstein-Raun teaches all the limitation of Claim 1. Weinstein-Raun further teach: controlling the vehicle based on the respective predicted multi-state classification comprises providing data specifying the respected predicted multi-category classification for the target agent to a planning system of the vehicle to generate planning decisions that plan a future trajectory of the vehicle (refer to the mapping in Claim 1 & 0086, 0073, 0070, “taking appropriate action in response ( e.g., by altering the path of the vehicle 10 accordingly based on the assessment)”; i.e., based on the probability of the assessed states/categories of the target vehicle, perform planning of the trajectory).
Regarding Claim 14, Weinstein-Raun teaches all the limitation of Claim 13. Weinstein-Raun further teach: each of the plurality of agents is an agent in a vicinity of the vehicle in the environment (refer to the mapping in Claim 1 & 0070, “assessing a target vehicle in proximity to the vehicle 10”), and the context data comprises data generated from data captured by one or more sensors of the vehicle (0067, “the computer vision system 74 can incorporate information from multiple sensors, including but not limited to cameras, lidars, radars, and/or any number of other types of sensors.”).
Regarding Claim 17 – 19, these are the corresponding system claim of Claim 1 – 3. Weinstein-Raun further teach: one or more computers, and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations (0061, “the user device can be realized in … a desktop computer, a mobile computer … realized as a computer-implemented or computer-based device having hardware, software, firmware and/or processing logic needed to carry out the various technique and methodologies described herein”). These claims are rejected with same reason.
Regarding Claim 20, Claim 20 is the corresponding computer-readable storage media claim of Claim 17. Claim 20 is rejected with same reason.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Weinstein-Raun, US20180079423 as applied to claim 5 above, and further in view of Gao et al., (hereinafter Gao), “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”.
Regarding Claim 6, Weinstein-Raun teaches all the limitation of Claim 5. Weinstein-Raun further teach: processing the context data using the neural network having the plurality of network parameters (refer to the mapping in Claim 5 & Weinstein-Raun 0109, “artificial neural networks (ANN)”; neural network is trained with plurality of parameters)
Weinstein-Raun does not explicitly teach:
generating, from the context data, (i) a respective road feature embedding for each of the plurality of road features, (ii) a respective agent embedding for each of the plurality of agents that characterizes a state of the agent at a current time point, and (iii) a respective traffic light embedding for each of the plurality of traffic light signals that characterizes a state of the traffic light signal at the current time point; and for each of the one or more target agents of the plurality of agents in the environment: generating, from the respective road feature embeddings, the respective agent embeddings, and the respective traffic light embeddings, (i) agent interaction embeddings characterizing the states of other agents in the environment relative to the target agent, and (ii) road feature interaction embeddings characterizing the plurality of road features in the environment relative to the target agent.
Gao, in the same field of endeavor, explicitly teach:
generating, from the context data, (i) a respective road feature embedding for each of the plurality of road features, (ii) a respective agent embedding for each of the plurality of agents that characterizes a state of the agent at a current time point, and (iii) a respective traffic light embedding for each of the plurality of traffic light signals that characterizes a state of the traffic light signal at the current time point (Gao, sec. 3.1, “Most of the annotations from an HD map are in the form of splines (e.g. lanes), closed shape (e.g. regions of intersections) and points (e.g. traffic lights), with additional attribute information such as the semantic labels of the annotations and their current states (e.g. color of the traffic light, speed limit of the road). For agents, their trajectories are in the form of directed splines with respect to time. All of these elements can be approximated as sequences of vectors”, “Our polyline subgraph network … embedding the ordering information into vectors”); and
for each of the one or more target agents of the plurality of agents in the environment: generating, from the respective road feature embeddings, the respective agent embeddings, and the respective traffic light embeddings, (i) agent interaction embeddings characterizing the states of other agents in the environment relative to the target agent, and (ii) road feature interaction embeddings characterizing the plurality of road features in the environment relative to the target agent (Gao, fig. 2 & sec. 1, the global interaction graph captures interaction embeddings between agents (the green blocks) and the interaction (road feature interaction embeddings) between each road feature embeddings/blue blocks and green blocks).
Weinstein-Raun and Gao both teach multi agent behavior prediction learning model for the autonomous vehicle and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further apply the VectorNet of Gao’s teaching to the system of Weinstein-Raun to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification for the model “outperforms the state of the art” (Gao, abs.).
Regarding Claim 7, Weinstein-Raun and Gao combination teaches all the limitation of Claim 6. The combination further teach: generating the respective road feature embedding for each of the plurality of road features comprises generating a respective polyline that represent the road feature (Gao, sec. 1, “geographic extent of the road features … can be closely approximated as polylines”).
Regarding Claim 8, Weinstein-Raun and Gao combination teaches all the limitation of Claim 6. The combination further teach: generating the respective road feature embedding for each of the plurality of road features comprises generating a respective polyline embedding for the respective polyline that represents the road feature using a road feature encoder neural network (Gao, sec. 3.1, “lanes … regions … intersections (road feature) … All of these elements can be approximated as sequences of vectors”, “we treat each vector vi belonging to a polyline Pj as a node in the graph with node features”; sec. 3.2, “Function genc(.) transforms the individual node features”, “genc(.) is a multi-layer perceptron (MLP)”; i.e., use MLP, which is a neural network, to transform/encode road feature into embeddings).
Regarding Claim 9, Weinstein-Raun and Gao combination teaches all the limitation of Claim 6. The combination further teach: generating the respective agent embedding and the respective traffic light embedding comprises: processing the one or more agent vectors using an agent encoder neural network to generate the respective agent embedding; and processing the one or more traffic light vectors using a traffic light encoder neural network to generate the respective traffic light embedding (Gao, sec. 3.1, “traffic lights … agents … All of these elements can be approximated as sequences of vectors”, “we treat each vector vi belonging to a polyline Pj as a node in the graph with node features”; sec. 3.2, “Function genc(.) transforms the individual node features”, “genc(.) is a multi-layer perceptron (MLP)”; i.e., use MLP, which is a neural network, to transform/encode traffic light and agent into embeddings).
Regarding Claim 10, Weinstein-Raun and Gao combination teaches all the limitation of Claim 9. The combination further teach: the agent encoder neural network and the traffic light encoder neural network are each a respective multi-layer perceptron or a recurrent neural network (Gao, sec. 3.2, “genc(.) is a multi-layer perceptron (MLP)”).
Regarding Claim 11, Weinstein-Raun and Gao combination teaches all the limitation of Claim 6. The combination further teach: in generating the agent interaction embeddings and the road feature interaction embeddings comprises: processing the respective road feature embeddings, the respective agent embeddings, and the respective traffic light embeddings using a self-attention neural network to generate the agent interaction embeddings and the road feature interaction embeddings (Gao, sec. 3.3, “We now consider modeling the high-order interactions on the polyline node features {p1, p2, …, pP} with a global interaction graph”, “Our graph network is implemented as a self-attention operation”).
Regarding Claim 12, Weinstein-Raun and Gao combination teaches all the limitation of Claim 6. The combination further teach: generating the predicted classification for the target agent comprises: processing the respective agent interaction embeddings and the respective road feature interaction embeddings using an output neural network to generate the predicted classification (Gao, sec. 1, “trajectories of other moving agents are propagated to the target agent node through the GNN. We can then take the output node feature corresponding to the target agent to decode its future trajectories”; sec. 3.3, “We now consider modeling the high-order interactions on the polyline node features {p1, p2, …, pP} with a global interaction graph”, “We then decode the future trajectories from the nodes corresponding the moving agents”, “we use an MLP as the decoder function”; i.e., use MLP, which is a neural network, to decode/process the graphical neural network, which models the interactions between nodes, to get the predicted result. Examiner notes that even though Gao’s final output is value instead of class as claimed, Weinstein-Raun teaches predicting the classes representing different state of the agent. The combination renders obviousness of the claimed limitation. ).
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Weinstein-Raun, US20180079423 as applied to claim 13 above, and further in view of Pink, US20140336912.
Regarding Claim 16, Weinstein-Raun teaches all the limitation of Claim 1. Weinstein-Raun further teach: generating the predicted classification for the target agent comprises: obtaining context data characterizing the environment comprises obtaining data indicating that the one or more target agents are stationary at a current time point (Weinstein-Raun, 0092, “percept nodes included … an observed movement state 708 as to whether or not the target vehicle is currently moving”),
Weinstein-Raun does not explicitly teach:
wherein the generating is performed only for target agents that are indicated as stationary at the current time point
Pink, explicitly teach:
wherein the generating is performed only for target agents that are indicated as stationary at the current time point (Pink, 0009, “upon detection of a stopped vehicle, and decision data being formed based on the starting intention criterion, which include a pieces of information on whether or not the stopped vehicle will start”; i.e., Pink teaches an intention prediction model that focus only on the stopped/stationary vehicle).
Weinstein-Raun and Pink both teach multi agent behavior prediction learning model for the autonomous vehicle and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further apply Pink’s control logic that applies specifically to the intention of stopped vehicle to the system of Weinstein-Raun to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to reduce the cost of time and energy (Pink, 0005).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Caruana, “MultiTask Learning” which teaches performing different type of machine learning tasks using common semantic/embedding layer/data with different output model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HELAL ALGAHAIM can be reached on (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIEN MING CHOU/Examiner, Art Unit 3666
/HELAL A ALGAHAIM/SPE , Art Unit 3666